Sideslip angle estimation considering short-duration longitudinal velocity variation

The accurate estimation of sideslip angle is necessary for many vehicle control systems. The detection of sliding and skidding is especially critical in emergency situations. In this paper, a sideslip angle estimation method is proposed that considers severe longitudinal velocity variation over the short period of time during which a vehicle may lose stability due to sliding or spinning. An extended Kalman filter (EKF) based on a kinematic model of a vehicle is used without initialization of the inertial measurement unit to estimate vehicle longitudinal velocity. A dynamic compensation method that compensates for the difference in the locations of the vehicle velocity sensor and the IMU in on-road vehicle tests is proposed. Evaluations with a CarSim™ 27-degree-of-freedom (DOF) model for various vehicle test scenarios and with on-road tests using a real vehicle show that the proposed sideslip angle estimation method can accurately predict sideslip angle, even when vehicle longitudinal velocity changes significantly.     

Vehicle distance estimation using a mono-camera for FCW/AEB systems

For robust vision-based forward collision warning (FCW) and autonomous emergency braking (AEB) systems, not only reliable detection performance including high detection rate and low false positives but also accurate measurement output of a target vehicle is required. Especially, in order to reduce false alarm or activation of FCW/AEB systems, the systems require the precise measurement output of a target object, such as position, velocity, acceleration, and time-to-collision (TTC). In this study, we developed a measurement estimation algorithm of a target vehicle using a monocular camera. This method estimates two cases of vehicle widths for a target vehicle by using the detected lane information and a pin-hole camera model. After that, the position, velocity, acceleration, and TTC of a target vehicle are estimated by using a Kalman filter for the each estimated vehicle width. To improve robustness, the both estimation results using the detected lane information and the pinhole camera model are fused. This estimation algorithm was evaluated and compared with the state-of-the-art technology. As a result, the proposed measurement output estimation method can improve the performance of the FCW/AEB systems.     

发动机制动工况下汽车制动器摩擦性能分析

建立了基于恒速制动车辆纵向力平衡方程、制动器耗散功率及其温度变化微分方程、管路压力调节等子模型的恒速长下坡汽车制动器摩擦性能分析系统.以两轴中型汽车为例,对前后制动器在不同挡位发动机制动时的温度、制动副摩擦因数、制动力分配及管路压力变化进行了计算.结果表明,在不影响车速情况下,合理使用各挡发动机制动可改善汽车前、后制动器热负荷,减小或避免制动摩擦力矩热衰退,保证汽车下长坡安全行驶.     

Estimation of longitudinal speed robust to road conditions for ground vehicles

This article seeks to develop a longitudinal vehicle velocity estimator robust to road conditions by employing a tyre model at each corner. Combining the lumped LuGre tyre model and the vehicle kinematics, the tyres internal deflection state is used to gain an accurate estimation. Conventional kinematic-based velocity estimators use acceleration measurements, without correction with the tyre forces. However, this results in inaccurate velocity estimation because of sensor uncertainties which should be handled with another measurement such as tyre forces that depend on unknown road friction. The new Kalman-based observer in this paper addresses this issue by considering tyre nonlinearities with a minimum number of required tyre parameters and the road condition as uncertainty. Longitudinal forces obtained by the unscented Kalman filter on the wheel dynamics is employed as an observation for the Kalman-based velocity estimator at each corner. The stability of the proposed time-varying estimator is investigated and its performance is examined experimentally in several tests and on different road surface frictions. Road experiments and simulation results show the accuracy and robustness of the proposed approach in estimating longitudinal speed for ground vehicles.     

Distributed and self-adaptive vehicle speed estimation in the composite braking case for four-wheel drive hybrid electric car

Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic Stability Program (ESP) sensor signals and multiple powersource signals. Firstly, the simulation platform of a 4WD hybrid electric car was established, which integrates an electronic-hydraulic composited braking system model and its control strategy, a nonlinear seven degrees-of-freedom vehicle dynamics model, and the Burckhardt tyre model. Secondly, combining the braking torque signals with the ESP signals, self-adaptive unscented Kalman sub-filter and main-filter adaptable to the observation noise were, respectively, designed. Thirdly, the fusion rules for the sub-filters and master filter were proposed herein, and the estimation results were compared with the simulated value of a real vehicle speed. Finally, based on the hardware in-the-loop platform and by picking up the regenerative motor torque signals and wheel cylinder pressure signals, the proposed speed estimation algorithm was tested under the case of moderate braking on the highly adhesive road, and the case of Antilock Braking System (ABS) action on the slippery road, as well as the case of ABS action on the icy road. Test results show that the presented vehicle speed estimation algorithm has not only a high precision but also a strong adaptability in the composite braking case.     

Design of Regenerative Anti-Lock Braking System Controller for 4 In-Wheel-Motor Drive Electric Vehicle with Road Surface Estimation

This paper presents a regenerative anti-lock braking system control method with road detection capability. The aim of the proposed methodology is to improve electric vehicle safety and energy economy during braking maneuvers. Vehicle body longitudinal deceleration is used to estimate a road surface. Based on the estimation results, the controller generates an appropriate braking torque to keep an optimal for various road surfaces wheel slip and to regenerate for a given motor the maximum possible amount of energy during vehicle deceleration. A fuzzy logic controller is applied to fulfill the task. The control method is tested on a four in-wheel-motor drive sport utility electric vehicle model. The model is constructed and parametrized according to the specifications provided by the vehicle manufacturer. The simulation results conducted on different road surfaces, including dry, wet and icy, are introduced.     

Feedback brake distribution control for minimum pitch

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution.     

Development of a rear-end collision avoidance system with automatic brake control

We have studied active safety technologies from the standpoint of “collision avoidance”. This paper describes a rear-end collision avoidance system with automatic brake control, which avoids a collision to the vehicle in front caused by inadvertent human errors using automatic emergency braking. The system is comprised of four key technological elements, headway distance measurement using scanning laser radar, path estimation algorithm with vehicle dynamics, collision prediction to the vehicle in front by a safe/danger decision algorithm, and longitudinal automatic brake control.     

Comparative study of vehicle tyre–road friction coefficient estimation with a novel cost-effective method

This paper qualitatively and quantitatively reviews and compares three typical tyre–road friction coefficient estimation methods, which are the slip slope method, individual tyre force estimation method and extended Kalman filter method, and then presents a new cost-effective tyre–road friction coefficient estimation method. Based on the qualitative analysis and the numerical comparisons, it is found that all of the three typical methods can successfully estimate the tyre force and friction coefficient in most of the test conditions, but the estimation performance is compromised for some of the methods during different simulation scenarios. In addition, all of these three methods need global positioning system (GPS) to measure the absolute velocity of a vehicle. To overcome the above-mentioned problem, a novel cost-effective estimation method is proposed in this paper. This method requires only the inputs of wheel angular velocity, traction/brake torque and longitudinal acceleration, which are all easy to be measured using available sensors installed in passenger vehicles. By using this method, the vehicle absolute velocity and slip ratio can be estimated by an improved nonlinear observer without using GPS, and the friction force and tyre–road friction coefficient can be obtained from the estimated vehicle velocity and slip ratio. Simulations are used to validate the effectiveness of the proposed estimation method.     

A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations

A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

Robust control of anti-lock brake system

A robust control algorithm for an anti-lock brake system is proposed. The method used is based on static-state feedback of longitudinal slip and does not involve controller scheduling with changing vehicle speed or road adhesion coefficient estimation. An improvement involving scheduling of longitudinal slip reference with longitudinal acceleration measurement is included. Electromechanical braking actuators are used in simulations, and the algorithm used in this study is shown to have high performance on roads with constant and varying adhesion coefficients, displaying nice robustness properties against large vehicle speed and road adhesion coefficient variations. Guidelines are provided for tuning controller gains to cope with unknown actuator delay and measurement noise.     

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

SUMMARY

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

4WD电动汽车转速闭环控制

研究了4WD电动汽车的转速闭环控制：提出了摸型跟踪2自由度转速闭环控制策略和基于观测器的车速估算方法。介绍了该控制方法的基本原理及实现技术通过轮毂电机加载试验台的硬件在回路仿真、9自由度整车模型弯道制动及分离系数路面仿真，验证了该控制方法的有效性。     

基于交互多模型的车辆质量与道路坡度估计（双语出版）

车辆结构参数和道路环境信息的实时准确获取是提高智能汽车运动控制性能的重要因素之一，而车辆质量与道路坡度信息是多种汽车控制系统的必要信息，因此质量与坡度在线估计的研究一直受到关注。针对车辆质量与道路坡度的联合估计问题，提出了一种基于交互多模型的质量与坡度融合估计方法。首先，设定了适宜进行质量精确估计的工况条件，据此提出了基于模糊规则的质量估计置信度因子计算算法，进而设计了基于置信度因子的递推最小二乘车辆质量估计算法，以实现质量的在线估计。然后，以车辆纵向动力学模型为基础，建立了运动学和动力学2种坡度估计模型，并设计了基于运动学模型的线性卡尔曼滤波坡度观测器，基于电子稳定性程序ESP的纵向加速度信息实现坡度估计，设计了基于动力学模型的无迹卡尔曼滤波坡度观测器，基于ESP和发动机管理系统EMS的力信息实现坡度估计。运动学模型未考虑车辆姿态信息，坡度估算结果与实际值有偏差；动力学模型对模型精度要求高，算法稳定性差，为充分发挥2种方法优势实现坡度的精确估计，采用交互多模型算法实现了2种坡度估计方法的加权融合。最后，对所设计的算法进行了实车试验验证。结果表明：所设计的质量与坡度估算算法具有较好的实时性和准确性，适合智能汽车运动控制的应用需求。     

Yaw rate and side-slip control considering vehicle longitudinal dynamics

Most conventional vehicle stability controllers operate on the basis of many simplifying assumptions, such as a small steering wheel angle, constant longitudinal velocity and a small side-slip angle. This paper presents a new approach for controlling the yaw rate and side-slip of a vehicle without neglecting its longitudinal dynamics and without making simplifying assumptions about its motion. A sliding-mode controller is used to develop a differential braking controller for tracking a desired vehicle yaw rate for a given steering wheel angle, while keeping the vehicle’s side-slip angle as small as possible. The trade-off that exists between yaw rate and side-slip control is described. Conventional and proposed algorithms are presented, and the effectiveness of the proposed controller is investigated using a seven-degree-of-freedom vehicle dynamics model. The simulation results demonstrate that the proposed controller is more effective than the conventional one.     

Non-singular slip (NSS) method for longitudinal tire force calculations in a sudden braking simulation

In a dynamic vehicle simulation, longitudinal tire force is primarily based on the longitudinal slip (ratio). In the longitudinal slip formula, state variables are used in the denominator. This causes a divergence problem for numerical simulations of vehicle dynamics. To avoid this numerical singularity, a differential slip calculation method was developed for use in dynamic vehicle simulations. However, this method also causes a singularity when the wheel velocity approaches zero in a pure slip state, such as during sudden braking. In this paper, a new longitudinal slip calculation method, which can overcome singularities in all velocity conditions, is proposed. For this purpose, the Taylor series is adapted to the slip formula and the idea of virtual wheel rotation stiffness is introduced for the development of the slip equation. The physical phenomenon at the zero slip state is analyzed. Finally, the proposed slip formula is used to solve the numerical singularity problem, and the non-singular slip (NSS) calculation method is proposed. The proposed NSS method is applied to tire model performance test (TMPT) simulations to validate its performance.     

基于智能电动汽车的纵向车速跟随控制策略

首先,设计了一种切换缓冲区间阈值可调的切换逻辑。其次,设计了一种对车速变化具有自适应性的PI控制驱动策略,再次,设计一种基于模糊控制的制动控制策略,在保证纵向车速跟踪精度的前提下,能避免车辆制动时的“前冲现象”,提高了舒适性。最后,通过Matlab与Carsim、Amesim的联合仿真验证了算法的有效性。     

车辆纵向速度估算算法发展现状综述

车辆的纵向车速是车辆主动安全系统的重要参考信息,在制动防抱死(ABS)和驱动防滑系统(ASR)中,纵向车速是计算纵向滑移率、保持车辆行驶稳定性的重要参数。文章对现存的车辆纵向速度算法进行了分类综述,将其分为基于基本信息的直接计算方法和基于模型信息的间接计算方法两大类,对各种方法的优缺点进行了讨论,并对其发展趋势进行了展望。